User customization and user optimization of cochlear implant devices

描述（由申请人提供）：目前人工耳蜗（CI）用户采用的适配方法福尔斯沿着“一刀切”的路线，因为假设诊所中的初始适配在所有听力环境中都应该同样有效。语音编码算法和相关参数（例如，压缩函数）是固定的并且对于不同的听音情况不被患者或临床医生以任何方式优化（例如，安静、音乐、噪音）。本研究通过使用便携式研究语音处理器解决了这一主要限制，该处理器允许用户：（a）基于两种不同的结果测量（主观质量评级和辅音清晰度）改变MAP参数（B）针对不同的收听情况定制MAP以获得最大益处，以及（c）收集反馈并允许记录真实的声学信号以供进一步分析。在这项研究中，单侧，双侧和双峰用户将参加双盲现场试验，以评估语音处理器的真正好处，可以很容易地定制的用户，以满足他们的需求的基础上主观质量评级或语音清晰度的措施。该项目的首要假设是，当人工耳蜗植入设备（由用户）针对各种听力情况进行定制时，可以在主观质量和可懂度方面获得最大益处。现场研究的结果将告诉我们CI用户在现实世界中遇到的困难，以及定制语音编码算法的价值和额外的好处，不同的环境，所有的CI用户的反馈的基础上。这样的反馈和用户交互不能使用现有的商业处理器收集，但它是迫切需要的，以推动该领域的发展。

DESCRIPTION (provided by applicant): The current fitting approach adopted for cochlear implant (CI) users falls along the lines of "one-size-fits-all", in that it is assumed that the initial fitting in the clinic should work equally well in all listening environments. Speech coding algorithms and associated parameters (e.g., compression function) are fixed and are not optimized in any way by either the patient or clinician for different listening situations (e.g., quiet, music, noise). The present study addresses this major limitation with the use of a portable research speech processor that allows users to: (a) change MAP parameters based on two different outcome measures (subjective quality ratings and consonant intelligibility) (b) customize the MAP to different listening situations for maximum benefit, and (c) collect feedback and allow the recording of real acoustic signals for further analysis. In this study, unilateral, bilateral and bimodal users will participate in double-blind field trials to assess the true benefit of a speech processor that can be easily customized by the users to fit their needs based on subjective quality ratings or speech intelligibility measures. The overarching hypothesis of this project is that maximum benefit, both in terms of subjective quality and intelligibility, can be obtained when the cochlear implant devices are customized (by the users) to the various listening situations. The outcomes of the field studies will tell us about the difficulties experienced by CI users in real-world situations as well as the value and additional benefit of customizing speech coding algorithms to different environments, all based on feedback from CI users. Such feedback and user interaction can not be gathered using existing commercial processors, yet it is critically needed to move the field forward.